The R markdown is available from the pulldown menu for Code at the upper-right, choose “Download Rmd”, or download the Rmd from GitHub.

Jupyter Bridge

Jupyter-Bridge is a Flask service that executes on a server accessible to both a remote Jupyter server and browser-based Jupyter client. Code running on the server calls Jupyter-Bridge to queue a request that the client will execute, and the client will use Jupyter-Bridge to return a reply. This enables a workflow running on remote Jupyter to execute functions on a PC-local Cytoscape – the remote Jupyter runs the request through Jupyter-Bridge, where it is picked up by Javascript code running on the Jupyter web page in the PC-local browser, which in turn calls Cytoscape. The Cytoscape response travels the reverse route.

Jupyter-Bridge allows a remote Jupyter Notebook to communicate with a workstation-based Cytoscape as if the Notebook were running on the Cytoscape workstation. A Jupyter Notebook passes a Cytoscape call to an independent Jupyter-Bridge server where it’s picked up by the Jupyter-Bridge browser component and is passed to Cytoscape. The Cytoscape response is returned via the opposite flow. As a result, workflows can reside in the cloud, access cloud resources, and yet still leverage Cytoscape features. Jupyter Bridge supports py4cytoscape first, and now RCy3 also support Jupyter-Bridge.

Visit the source code of Juputer Bridge for more information.

Prerequisites (Local machine)

RCy3 works by connecting with Cytoscape. You will need to install and launch Cytoscape in your local machine:

Prerequisites (Cloud server)

There are a lot of cloud computing services online, such as Google Colab, Amazon EMR Notebook, Microsoft Azure, CoCalc and your own JupyterHub. You can choose your favorite one.

Here we use Google Colab to demonstrate. Visit this link to create a new empty R Notebook, and make sure to run code below in the cloud.

Installation

Install the latest version of RCy3 in the cloud server from GitHub.

install.packages("devtools")
library(devtools)
install_github("cytoscape/RCy3")
library(RCy3)

Connect to local Cytoscape

First, build connection between the jupyter notebook and local Cytoscape.

browserClientJs <- getBrowserClientJs()
IRdisplay::display_javascript(browserClientJs)

Check connection

Then, launch Cytoscape and keep it running whenever using RCy3 and Jupyter Bridge. Confirm that you have everything installed and running:

cytoscapeVersionInfo()
      apiVersion cytoscapeVersion 
            "v1"          "3.8.2" 
cytoscapePing()
You are connected to Cytoscape!

Done! Now you can execute a workflow in a remote server-based Jupyter Notebook to leverage your workstation’s Cytoscape. You can also easily share notebook-based workflows and data sets.

Use case: Run differentially expressed genes network analysis in the cloud

Visit the Jupyter Bridge RCy3 and Differentially Expressed Genes Network Analysis for the detailed workflow.

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